DICOM WG21MULTI-ENERGY IMAGING SUPPLEMENT OVERVIEW

16-June-2015

Agenda• Definitions, Use Cases, Objectives• Aspects of Multi-Energy (ME) technologies• New types of ME images• Proposed Approach• Risk and Concerns

ME-Definition

Imaging techniques, including scanning, reconstruction, processing, when the scanner utilizes multiple energies from the X-Ray beam spectrum, as opposed to the conventional CT imaging, when a single (accumulated) X-Ray spectrum is used.

The existing CT and Enhanced CT (eCT) IODs do not adequately describe the new CT multi-energy imaging. Although different vendors apply different scanning and detection techniques to achieve multi-energy images, there is large commonality in the generated diagnostic images.

Examples of Multi-Energy CT

Physics background

Clinical Use Cases

The primary potential applications this supplement intends to focus on include:• Allowing better differentiation of materials that look similar

on conventional CT images, e.g., to differentiate Iodine and Calcium in vascular structures

• Eliminating acquisitions such as non-contrast acquisition, when the “virtual/artificial non-contrast” image is generated from the contrast image

Objectives

When defining this supplement, the following objectives / goals have been considered:

1. Making multi-energy information (acquisition, reconstruction and processing attributes) available to rendering or processing applications

2. Facilitating fast and easy adoption of this supplement across the imaging community, both modalities and PACS/Displays.

3. Eliminating (or at least minimizing) the risk of mis-interpretation when the ME images are displayed by a non-compliant display, including incorrect measurements

New aspects of ME technologies

Different vendors apply different technologies for:• Scanning• Detection• Reconstruction• Processing• Material decomposition

This results in a variety of image types calling for standardization of parameters and definitions

Virtual Mono-chromatic

Image (VMI)

Material-Specific Image

Material-Subtracted

Image

Color Overlay Image

Color Blending

Image

Discrete LabelingImage

Proportional Map Image

Iodine Map;Bone Density

Virt. Non-C; Virt. Non-Ca

Effective AN (Z) Image

Electron Density Image

Probability Map Image

Color Map Image

Multi-Energy Imaging

Material Quantification

(Decomposition)

Material Classification

(Labeling)

Material Visualization

(Color)

Material-Modified Image

Highlighted;Partially-Suppressed

Color Eff. AN

Gout crystals on top of CT image

4 categories

many flavors

Proposed approach• For VMI and Quantification we reuse basic and enhanced CT

IOD‘s for ME images • For Classification we consider CT IOD, Segmentation IOD or

Parametric Maps IOD• For Visualization we consider reusing SC, Presentation States,

Blending and may be Parametric map or adding RGB palette information to CT IOD

• Include the new attributes (add macros) in the existing IOD‘s• Add new definitions to the existing standard (like Image Type,

defined terms)• Adapt some descriptions to multi-energy related cases

Close to current implementations. Better chance to be widely/fast adopted

Virtual Mono-chromatic Image (VMI)

40 keV 167 keV

Essentially analogous to a CT image that would be generated by a monochromatic (of a specific keV value) X-Ray beam

Material-Specific ImageIodine Map

Image presenting a physical scale of specific material. Pixel values can be in HU or in equivalent material concentration (e.g., mg/ml).

Material-Subtracted ImageVirtual Non-Contrast Image

Image with one or more materials subtracted. Pixel values may have been corrected for displacement of one material by another material.

Material-Modified ImageImage where pixel values have been modified to highlight a certain target material (either by partially suppressing the background or by enhancing the target material), or to partially suppress the target material.

Unlike Material-Specific and Material-Subtracted images (that can allow accurate measurements), the Material-Modified image is primary used for better visualization of the target materials

Bone Marrow Image

Electron Density ImageAn image where each pixel represents a number of electrons per unit volume. Widely used in radiotherapy.

Effective AN (Z) ImageAn image where each pixel represents Effective Atomic Number (aka “Effective Z”) of that pixel.

Probability Map ImageAn image where pixels describe the probability that this pixel is classified as one or more of the multiple defined materials

Gout Material Map

Material Visualization (Color)Users are asking to visualize ME-content in certain ways: color maps, color overlays, blending, etc.

How we intend to extend• C.8.2.1 CT Image Module

• CT Additional X-Ray Source Sequence (0018,9360) (modified)• Table X-1 “Optional CT Multi-Energy Macro Attributes" (added)

• CT Multi Energy Acquisition Sequence (1C)• >Table X-2 “CT Multi-Energy Acquisition Macro Attributes”

• CT Multi Energy Material Decomposition Sequence (1C)• >Table X-3 “CT Multi-Energy Material Decomposition Macro Attributes”

• CT Multi Energy Image Sequence (1C)• >Table X-4 “CT Multi-Energy Image Macro Attributes”

• C.8.15.3.9 CT X-Ray Details Macro (for Enhanced CT IOD only)• CT X-Ray Details Sequence

Risk and Concerns• Enhancements can be mis-interpreted in keV image

need for correct display label including keV image type and keV value

100 kV (conventional)50keV (virtual monochomatic)

Risk and Concerns• High keV (>150) images can be mis-interpreted that no

contrast was applied (missing contrast information)• Potential risk for VNC images (e.g. changing size of small

structures) – currently not broadly validated that VNC is truly equivalent to TrueNonContrast

• Quantitative measurements are different in non-contrast vs. contrast use case (e.g. renal mass and renal cyst)

• Missing labeling can cause confusion which can slow down the workflow delay the diagnosis

QuestionsTo guarantee that the created ME images contain the necessary information, a number of new attributes will be defined. These attributes will be added to the ME images, either as an extension to the existing IODs, or as part of new, ME-specific, IODs. However, we cannot mandate using any new specific attribute for existing IOD. Is there any workaround:

• Can we say that specific attribute is Type 1 if the system supports ME imaging? Yes only for new attributes;

• What about standard attributes, like Image Type? Ok to extend with new values and make them mandatory if the image is ME?

• Can we say that specific attribute is Type 1C if the units for pixels are not in HU? Not for existing attributes; ok for new

• Shall we rather define an optional SQ with mandatory ME attributes?• Image Type of VMI Images. It is defined use ORIGINAL unless there is a

specific case requiring it to be DERIVED. WG6 recommends leaving it to the vendor to decide if the image is ORIGINAL or DERIVED.

Questions

To add Real-World Value Mapping to CT IOD to accurately describe the non-HU values. There are several concerns with this approach:

• RWVM is not specified today for CT images or widely implemented in the field (is this correct?). As a result, the units can be misinterpreted by the display application

• Rescale Slope and Rescale Intercept are Type-1 for CT image (if it is ORIGINAL); there is potential conflict between rescale attributes and RWVM. If we define image type as DERIVED, we avoid Type-1 requirement for Rescale Slope/Intercept, so they can be omitted.

• As a consequence, Rescale attributes shall be used if possible; for specific cases (to be identified) RWVM may be considered.

Questions• To describe the need and recommendations for good

labeling in the informative section (e.g., to display keV value for VMI images; to display material + concentration for measurements, etc.). DICOM alone cannot enforce PACS/Workstation to present specific attributes therefore there is little chance new important attributes we introduce here will be presented to the users. Should we work with IHE?

• Can we “orchestrate” the object in such a way that naïve display will either present the image adequately or fail to present anything? For instance, we can put rescale attributes to zero for non-HU ME images

END OF PRESENTATION

Virtual Mono-chromatic Image (VMI)

43 HU (160 keV)

307 HU (50 keV)

31 HU (≈ 120kV)

- =

Contrast VMI Images

12 HU

206 HU

Renal Mass use case

Non-Contrast Image

DETAILS OF IMPLEMENTATION

ME Acquisition Techniques• X-Ray Sources

• SINGLE_SOURCE

• MULTI_SOURCE

• KV Switching• NONE, FAST, SLOW

• Multi-Energy Acquisition• SINGLE_SCAN

• MULTI_SCAN

• Multi-Energy Detection• CONVENTIONAL

• MULTILAYER

• PHOTON_COUNTING

• Technique-Specific Parameters:

• What do we want to record for Dual-Layer and PhC?

ME Material Decomposition• Decomposition Method

• SINOGRAM_BASED

• IMAGE_BASED

• Decomposition Base Materials (sequence)

• Decomposition Description• Vendor-specific label/description

• Material Attenuation Curves (opt)

• Decomposition Parameters• e.g., dual-energy ratio

Pixel Value Units• Method 1: using Rescale

• Rescale Slope• Rescale Intercept• Rescale Type • Measurement Units as standard Coded Values

• e.g., UCUM: "mg/cm^3“

• Method 2 – using Real World Value Mapping Sequence• First/Last Values Mapped• Value Slope/Intercept• LUT (optional)• Measurement Units (Coded Value)

Material Classification (Labeling)

20: Unknown

1: Material A

2: Material B

3: Material C

Discrete Labeling (most-probable material):A

BC

D1.5

Proportional/Density Map:

% or mg/ml

0.2

Probability/Confidence Map:

Spectral Imaging Challenges• Monochromatic Images

• Capture keV value• How to differentiate from “legacy” CT?

• Incl. query, display annotations

• Material Density Images• Non-HU values: how to avoid confusion?• Several alternatives for solution

• Segmentation IOD• Real-World Mapping• PTE-like new IOD• Rely on specific Rescale Intercept/Slope/Type

• Effective Atomic Number Images• Similar challenges as for Material Density• Using Color mapping prohibits measurements and analysis

Open Issues (some)• Assess risks ME images been misinterpreted as the

conventional ones on a PACS/Workstation• AI: work through Mark Armstrong (ACR) to get radiologist to

enumerate the risks

• How shall we model KV Switching? (including duration and gaps, proportion of High/Low KV)• Alternative 1: Describe as two different sources• Alternative 2: Single dynamic source KV-Switching specific

parameters

• How shall we “model” Photon Counting detector?• Shall we better describe the full “Data Path”?

• Too complicated and vendor-specific?

• Each vendor to provide a list of potential public attributes specific for the each ME Acquisition/Recon techniques